Clinical analytics may be defined as a set of methodologies, tools, and information technology infrastructure that transforms raw healthcare data into meaningful and useful information, knowledge, and visualization that enables effective insights and decision-making for clinical users. In practice, it has proven to be difficult to actually make effective use of the resulting clinical analytics data to improve health-related outcomes. Further, the analytics are only as good as the accuracy of the incoming data. In the healthcare field, comprehensive and accurate data regarding a person's health-related history is critical to the generation of useful analytics results. Efforts to assure continuity of care, accurate record keeping, effective follow-up and preventive care, prompt payment, and detection of fraud, waste, and abuse all would benefit from the availability of accurate analytics. A particular challenge to analytics accuracy arises because the gamut of personal attributes commonly used to identify a person (for example, name, birth date, and sex) is rarely captured in the same manner by each entity in the health care system; accordingly, rarely is comprehensive health-related data for the same individual actually attributed to that individual. Identification of proper records is key to the generation of useful analytics. Accordingly, there is a specific need for a process that allows for the rapid and accurate identification of the proper records and their integration for the purpose of providing high quality, patient-focused care.
Further, while there are tools and vendors for performing analytics on available data, including data management, stratification and predictive modeling, technical challenges remain with respect to the availability of data for performing analytics, as well as the significant challenge which exists for payers attempting to actually integrate the resulting analytics data into care management solutions. Exemplary analytics vendors include, for example, MEDai (an Elsevier company at the time of filing of this application). Accordingly, there is a need for systems and processes that effectively integrate medical data sources with analytics capabilities to provide both data-in and data-out solutions that are useful for a range of recipients to improve health-related outcomes.